[01] Mohammadreza oor Karimip, Department of Electronics, Faculty of Engineering, Shahrood Science and Research Branch, Islamic Azad University, Shahrood, Iran. [02] Vahid Abolghasemi, Faculty of Electrical Engineering and Robotics, University of Shahrood, Shahrood, Iran. [03] Saideh Ferdowsi, Faculty of Electrical Engineering and Robotics, University of Shahrood, Shahrood, Iran.

In this paper, a denoising method based on dictionary learning has been proposed. With the increasing use of digital images, the methods that can remove noise based on image content and not restrictedly based on statistical properties has been widely extended. The major weakness of dictionary learning methods is that all of these methods require a long training process and a very large storage memory for storing features extracted from the training images. In the proposed method, using the concept of sparse matrix and similarities between samples extracted of similar images and adaptive filters the training process of dictionary based on ideal images have been simplified. Finally Images are checked based on its content by implicit optimization of memory usage and image noise will be removed with a minimum loss of stored samples in existing dictionary. At the end, the proposed method is implemented and results are shown its capabilities in comparison with other methods.

Denoising, Sparsity, Clustering, Kmeans, Dictionary Learning

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